Developing and deploying a use-inspired metapopulation framework for stratified health outcomes

Winter Simulation Conference, 2025

Presenter: Arindam Fadikar

Argonne National Laboratory

2025-12-08

People involved

Roadmap

  • Motivation & collaboration context
  • MetaRVM modeling framework
    • Metapopulation structure
    • Disease progression model
    • Mixing matrices from a synthetic population
  • Implementation as an R package
  • Trajectory-oriented optimization
  • Case study: influenza in Chicago

Motivation

  • Public health decision makers increasingly rely on epidemiological models to:
    • Forecast likely futures
    • Stress-test interventions
    • Plan resource allocation (e.g., beds, staffing)

Motivation

Population-based ODE model

  • Aggregate-level dynamics
  • Homogeneous mixing assumptions
  • Computationally light and fast

Agent-based model

  • Individual-level interactions
  • Heterogeneous contact network
  • Expensive to run for large population

Motivation

Population-based ODE model

  • Aggregate-level dynamics
  • Homogeneous mixing assumptions

MetaRVM

  • Stratified metapopulation structure
  • Time-varying mixing from synthetic contacts

Agent-based model

  • Individual-level interactions
  • Heterogeneous contact network

MetaRVM

  • An open-source R package for modeling infectious disease spread across stratified subpopulations.
  • Subpopulations can be defined by:
    • Geography (e.g., neighborhood, ZIP, zone)
    • Demographics (e.g., age, race/ethnicity)
  • Features:
    • Time varying mixing pattern
    • Extended SEIR-type disease progression
    • Checkpointing functionality
    • Integration with Trajectory-Oriented Optimization workflows

Model structure: extended SEIR

Core disease states (example):

  • S – Susceptible
  • E – Exposed (infected, not yet infectious)
  • I – Infectious (possibly with severity subcompartments)
  • H – Hospitalized
  • R – Recovered / immune
  • V – Vaccinated

Key design choices:

  • Discrete-time simulation, typically daily time steps
  • Transitions governed by:
    • Latent and infectious periods
    • Age- and stratum-specific risks
    • Vaccination efficacy and waning, if modeled
  • Outputs:
    • Incident infections, hospitalizations, etc.
    • Cumulative outcomes by stratum

Force of infection and mixing

  • For each stratum, the force of infection depends on:
    • Contact rates between strata
    • Prevalence of infectious individuals in each stratum
  • Let:
    • β(t) be a time-varying transmission parameter
    • M(t) be a mixing matrix (strata × strata) at time t
    • I(t) the vector of infectious counts by stratum
  • Conceptually:
    • Infection pressure in stratum k combines:
      • Its own infectious individuals
      • Contacts with infectious individuals in other strata
    • With separate terms for susceptible vs. vaccinated if modeled

Implementation uses efficiently stored and processed mixing matrices derived from a synthetic population.

Building mixing matrices from a synthetic population

  • Underlying asset: synthetic population for Chicago
    • Statistically representative of residents and households
    • Includes:
      • Demographics (age, etc.)
      • Home, work, school, and activity locations
      • Daily schedules / contact opportunities
  • From this, we construct empirical mixing matrices:
    • Count cross-stratum contacts in:
      • Households
      • Schools
      • Workplaces
      • Other locations
    • Aggregate to daily or weekly matrices
    • Separate weekday / weekend patterns

This leverages the richness of CityCOVID’s synthetic ecosystem, but in a metapopulation abstraction.

Example: weekday vs weekend mixing

  • Weekday:
    • Strong school-based mixing among children
    • Workplace mixing among working-age adults
    • Cross-age mixing in households in mornings and evenings
  • Weekend:
    • Less structured mixing
    • More household and community mixing
  • MetaRVM:
    • Can use separate mixing matrices for:
      • Weekdays vs weekends
      • Different phases of the season
      • Intervention periods (e.g., school closures)

Workflow: from data to decision

  1. Define stratification
    • e.g., age × neighborhood, or age × race
  2. Prepare inputs
    • Population counts by stratum
    • Daily or weekly mixing matrices
    • Vaccination and other intervention schedules
  3. Configure MetaRVM
    • Structural and epidemiological parameters
    • Simulation horizon and time step
  4. Calibrate to observed data
    • Hospitalizations, case counts, etc.
    • Use Bayesian optimization / calibration tools
  5. Run scenarios
    • Alternative vaccination strategies
    • Timing and intensity of NPIs
  6. Summarize and communicate
    • Visualizations
    • Key metrics for decision makers

Case study: influenza hospitalizations in Chicago (2023–24)

  • Objective:
    • Support prospective surveillance of influenza-related hospitalizations in Chicago
  • Setup:
    • Strata: age groups (e.g., children, adults, seniors)
    • Observed data:
      • Daily or weekly influenza-related hospitalizations
    • Inputs:
      • Synthetic population-based mixing
      • Empirical vaccination curves over the season
  • Tasks:
    • Calibrate MetaRVM to observed hospitalizations
    • Generate short-term forecasts
    • Explore alternative vaccination scenarios

Calibration: overview

  • Calibration goal:
    • Align model-generated hospitalizations with observed time series
  • Approach:
    • Choose a set of calibration parameters (e.g., transmission scaling, reporting/observation fraction)
    • Define a loss function (e.g., squared error between modeled and observed hospitalizations)
    • Use Bayesian optimization (or other search strategies) to:
      • Explore parameter space efficiently
      • Balance exploitation and exploration
  • Outcome:
    • Posterior-like distribution over plausible parameter values
    • Uncertainty envelopes around model trajectories

Limitations and extensions

Limitations:

  • Still an aggregated model:
    • Does not capture individual-level heterogeneity beyond stratification
  • Dependent on:
    • Quality of the synthetic population and mixing matrices
    • Quality and timeliness of surveillance data
  • Calibration challenges:
    • Parameter identifiability
    • Structural uncertainty

Possible extensions:

  • Co-circulation of multiple pathogens (e.g., flu + RSV + COVID-19)
  • More nuanced interventions:
    • Layered NPIs, dynamic behavior change
    • Targeted vaccination by geography and risk
  • Deeper integration with:
    • High-performance computing resources
    • Automated data ingestion pipelines

Summary

  • MetaRVM provides a use-inspired metapopulation modeling framework for:
    • Detailed, stratified tracking of health outcomes
    • Infectious disease spread across multiple interacting subpopulations
  • Key contributions:
    • Uses synthetic population-derived mixing matrices
    • Balances realism with computational efficiency
    • Delivered as an open-source R package with a Shiny front end
  • Case study:
    • Demonstrated on influenza-related hospitalizations in Chicago
    • Supports prospective surveillance and scenario analysis

Looking ahead

  • Research directions:
    • Multi-pathogen modeling
    • Enhanced calibration and uncertainty quantification
    • Integration of behavioral and policy feedbacks
  • Deployment directions:
    • Broader adoption at other health departments
    • Sharing of configuration templates and best practices
    • Training materials for non-modeling stakeholders

Acknowledgments

  • Chicago Department of Public Health
  • Collaborators at Argonne National Laboratory and University of Chicago
  • Funding and institutional support as detailed in the paper

Questions?

Thank you!